This thesis explores temporal structure based on self-similarity in different

contexts.

An efficient dynamic programming algorithm is presented which discovers temporal

structures in music shows, obtains high quality results, and compares them to

similar algorithms used in the literature.  The program segments a

self-similarity matrix given a cost function and a fixed number of homogeneous

temporal structures to find. This is the initial approach we use to discover

temporal structures in music data.

The use of a self-similarity matrix to visualize temporal structures is

discussed in detail. Then the following question is explored; if similar

temporal structures in other corpora existed; could forecasting algorithms be

adapted to take advantage of them even if they were not known a priori?

Prediction with expert advice techniques are then introduced to exploit a priori

unknown temporal structures of a similar configuration in an on-line

configuration. Uni-variate Russian Stock Exchange options futures volatility

corpora are used, which are highly interesting for on-line forecasting.

We experiment with merging together expert models which have been trained in

some way to recognise temporal structures in corpora. The first types are kernel

ridge regression models trained to be experts on particular regions in time, or

untrained and given random sets of parameters which may work well on certain

time regions. The other types of model used are parsimonious predictors which

transform uni-variate financial data into elementary time series based on

homogeneous vicinities of information in the side domain. Expert merging

techniques are then used across these time series which produce a

validation-free forecaster comparable to sliding kernel ridge regression.